Abstract: The bipolar neutrosophic set is a suitable instrument to tackle the information with vagueness, complexity, and uncertainty. In this study, we improved the original EDAS (the evaluation based on distance from average solution) with bipolar neutrosophic numbers (BNNs) for a multiple-criteria group decision-making (MCGDM) problem. We calculated the average solution under all the criteria by two existing aggregation operators of BNNs. Then, we computed the positive distance and the negative distance from each alternative to the average ideal solution and determined the appraisal score of alternatives. Based on these scores, we obtained the ranking result. Finally, we demonstrated the practicability, stability, and capability of the improved EDAS method by analyzing the influence parameters and comparing results with an extended VIKOR method. PubDate: Fri, 15 Oct 2021 11:50:01 +000
Abstract: The growth in size and complexity of convolutional neural networks (CNNs) is forcing the partitioning of a network across multiple accelerators during training and pipelining of backpropagation computations over these accelerators. Pipelining results in the use of stale weights. Existing approaches to pipelined training avoid or limit the use of stale weights with techniques that either underutilize accelerators or increase training memory footprint. This paper contributes a pipelined backpropagation scheme that uses stale weights to maximize accelerator utilization and keep memory overhead modest. It explores the impact of stale weights on the statistical efficiency and performance using 4 CNNs (LeNet-5, AlexNet, VGG, and ResNet) and shows that when pipelining is introduced in early layers, training with stale weights converges and results in models with comparable inference accuracies to those resulting from nonpipelined training (a drop in accuracy of 0.4%, 4%, 0.83%, and 1.45% for the 4 networks, respectively). However, when pipelining is deeper in the network, inference accuracies drop significantly (up to 12% for VGG and 8.5% for ResNet-20). The paper also contributes a hybrid training scheme that combines pipelined with nonpipelined training to address this drop. The potential for performance improvement of the proposed scheme is demonstrated with a proof-of-concept pipelined backpropagation implementation in PyTorch on 2 GPUs using ResNet-56/110/224/362, achieving speedups of up to 1.8X over a 1-GPU baseline. PubDate: Wed, 22 Sep 2021 06:05:01 +000
Abstract: The banking industry is a market with great competition and dynamism where organizational performance becomes paramount. Different indicators can be used to measure organizational performance and sustain competitive advantage in a global marketplace. The execution of the performance indicators is usually achieved through human resources, which stand as the core element in sustaining the organization in the highly competitive marketplace. It becomes essential to effectively manage human resources strategically and align its strategies with organizational strategies. We adopted a survey research design using a quantitative approach, distributing a structured questionnaire to 305 respondents utilizing efficient sampling techniques. The prediction of bank performance is very crucial since bad performance can result in serious problems for the bank and society, such as bankruptcy and negative influence on the country’s economy. Most researchers in the past adopted traditional statistics to build prediction models; however, due to the efficiency of machine learning algorithms, a lot of researchers now apply various machine learning algorithms to various fields, including performance prediction systems. In this study, eight different machine learning algorithms were employed to build performance models to predict the prospective performance of commercial banks in Nigeria based on human resources outcomes (employee skills, attitude, and behavior) through the Python software tool with machine learning libraries and packages. The results of the analysis clearly show that human resources outcomes are crucial in achieving organizational performance, and the models built from the eight machine learning classifier algorithms in this study predict the bank performance as superior with the accuracies of 74–81%. The feature importance was computed with the package in Scikit-learn to show comparative importance or contribution of each feature in the prediction, and employee attitude is rated far more than other features. Nigeria’s bank industry should focus more on employee attitude so that the performance can be improved to outstanding class from the current superior class. PubDate: Tue, 21 Sep 2021 06:50:01 +000
Abstract: Cloud computing is a technology that allows dynamic and flexible computing capability and storage through on-demand delivery and pay-as-you-go services over the Internet. This technology has brought significant advances in the Information Technology (IT) domain. In the last few years, the evolution of cloud computing has led to the development of new technologies such as cloud federation, edge computing, and fog computing. However, with the development of Internet of Things (IoT), several challenges have emerged with these new technologies. Therefore, this paper discusses each of the emerging cloud-based technologies, as well as their architectures, opportunities, and challenges. We present how cloud computing evolved from one paradigm to another through the interplay of benefits such as improvement in computational resources through the combination of the strengths of various Cloud Service Providers (CSPs), decrease in latency, improvement in bandwidth, and so on. Furthermore, the paper highlights the application of different cloud paradigms in the healthcare ecosystem. PubDate: Mon, 20 Sep 2021 14:20:01 +000
Abstract: Facial expression is an important form of nonverbal communication, as it is noted that 55% of what humans communicate is expressed in facial expressions. There are several applications of facial expressions in diverse fields including medicine, security, gaming, and even business enterprises. Thus, currently, automatic facial expression recognition is a hotbed research area that attracts lots of grants and therefore the need to understand the trends very well. This study, as a result, aims to review selected published works in the domain of study and conduct valuable analysis to determine the most common and useful algorithms employed in the study. We selected published works from 2010 to 2021 and extracted, analyzed, and summarized the findings based on the most used techniques in feature extraction, feature selection, validation, databases, and classification. The result of the study indicates strongly that local binary pattern (LBP), principal component analysis (PCA), saturated vector machine (SVM), CK+, and 10-fold cross-validation are the most widely used feature extraction, feature selection, classifier, database, and validation method used, respectively. Therefore, in line with our findings, this study provides recommendations for research specifically for new researchers with little or no background as to which methods they can employ and strive to improve. PubDate: Sat, 18 Sep 2021 05:50:01 +000
Abstract: Unsaturated soils used as compacted subgrade, backfill, or foundation materials react unfavorably under hydraulically bound environments due to swell and shrink cycles in response to seasonal changes. To overcome these undesirable conditions, additive stabilization processes are used to improve the volume change phenomenon in soils. However, the use of supplementary binders made from solid waste base powder materials has become necessary to deal with the hazards of greenhouse due to ordinary cement use. Meanwhile, several studies are being carried out to design infrastructures even with the limitations of insufficient or lack of equipment needed for efficient design performance. Intelligent prediction techniques have been used to overcome this shortcoming as the primary purpose of this research work. Therefore, in this work, genetic programming (GP) and artificial neural network (ANN) have been used to predict the consistency limits, i.e., liquid limits, plastic limit, and plasticity index of unsaturated soil treated with a composite binder known as hybrid cement (HC) made from blending nanostructured quarry fines (NQF) and hydrated-lime-activated nanostructured rice husk ash (HANRHA). The database needed for the prediction operation was generated from several experiments corresponding with treatment dosages of HANRHA between 0 and 12% at a rate of 0.1%. The results of the stabilization exercise showed substantial development on the soil properties examined, while the prediction exercise showed that ANN outclassed GP in terms of performance evaluation, which was conducted using sum of squared error (SSE) and coefficient of determination (R2) indices. Generally, nanostructuring of the component binder material has contributed to the success achieved in both soil improvement and efficiency of the models predicted. PubDate: Tue, 27 Jul 2021 12:05:00 +000
Abstract: Although the Multihoist Scheduling Problem (MHSP) can be detailed as a job-shop configuration, the MHSP has additional constraints. Such constraints increase the difficulty and complexity of the schedule. Operation conditions in chemical processes are certainly different from other types of processes. Therefore, in order to model the real-world environment on a chemical production process, a simulation model is built and it emulates the feasibility requirements of such a production system. The results of the model, i.e., the makespan and the workload of the most loaded tank, are necessary for providing insights about which schedule on the shop floor should be implemented. A new biobjective optimization method is proposed, and it uses the results mentioned above in order to build new scenarios for the MHSP and to solve the aforementioned conflicting objectives. Various numerical experiments are shown to illustrate the performance of this new experimental technique, i.e., the simulation optimization approach. Based on the results, the proposed scheme tackles the inconvenience of the metaheuristics, i.e., lack of diversity of the solutions and poor ability of exploitation. In addition, the optimization approach is able to identify the best solutions by a distance-based ranking model and the solutions located in the first Pareto-front layer contributes to improve the search process of the aforementioned scheme, against other algorithms used in the comparison. PubDate: Wed, 07 Jul 2021 07:35:01 +000
Abstract: The design of a robust human identification system is in high demand in most modern applications such as internet banking and security, where the multifeature biometric system, also called feature fusion biometric system, is one of the common solutions that increases the system reliability and improves recognition accuracy. This paper implements a comprehensive comparison between two fusion methods, named the feature-level fusion and score-level fusion, to determine which method highly improves the overall system performance. The comparison takes into consideration the image quality for the six combination datasets as well as the type of the applied feature extraction method. The four feature extraction methods, local binary pattern (LBP), gray-level co-occurrence matrix (GLCM), principle component analysis (PCA), and Fourier descriptors (FDs), are applied separately to generate the face-iris machine vector dataset. The experimental results highlighted that the recognition accuracy has been significantly improved when the texture descriptor method, such as LBP, or the statistical method, such as PCA, is utilized with the score-level rather than feature-level fusion for all combination datasets. The maximum recognition accuracy is obtained at 97.53% with LBP and score-level fusion where the Euclidean distance (ED) is considered to measure the maximum accuracy rate at the minimum equal error rate (EER) value. PubDate: Tue, 22 Jun 2021 07:35:03 +000
Abstract: For most real-world data streams, the concept about which data is obtained may shift from time to time, a phenomenon known as concept drift. For most real-world applications such as nonstationary time-series data, concept drift often occurs in a cyclic fashion, and previously seen concepts will reappear, which supports a unique kind of concept drift known as recurring concepts. A cyclically drifting concept exhibits a tendency to return to previously visited states. Existing machine learning algorithms handle recurring concepts by retraining a learning model if concept is detected, leading to the loss of information if the concept was well learned by the learning model, and the concept will recur again in the next learning phase. A common remedy for most machine learning algorithms is to retain and reuse previously learned models, but the process is time-consuming and computationally prohibitive in nonstationary environments to appropriately select any optimal ensemble classifier capable of accurately adapting to recurring concepts. To learn streaming data, fast and accurate machine learning algorithms are needed for time-dependent applications. Most of the existing algorithms designed to handle concept drift do not take into account the presence of recurring concept drift. To accurately and efficiently handle recurring concepts with minimum computational overheads, we propose a novel and evolving ensemble method called Recurrent Adaptive Classifier Ensemble (RACE). The algorithm preserves an archive of previously learned models that are diverse and always trains both new and existing classifiers. The empirical experiments conducted on synthetic and real-world data stream benchmarks show that RACE significantly adapts to recurring concepts more accurately than some state-of-the-art ensemble classifiers based on classifier reuse. PubDate: Thu, 10 Jun 2021 11:20:02 +000
Abstract: South Africa has been classified as one of the most homicidal, violent, and dangerous places across the globe. However, the two elements that pushed South Africa high in the crime rank are the rates of social violence and homicide. It was reported by Business Insider that South Africa is among the most top 15 ferocious nations on earth. By 1995, South Africa was rated the second highest in terms of murder. However, the crime rate has reduced for some years and suddenly rose again in recent years. Due to social violence and crime rates in South Africa, foreign investors are no longer interested in continuing or starting a business with the nation, and hence, its economy is declining. South Africa’s government is looking for solutions to the crime issue and to redeem the image of the country in terms of high crime ranking and boost the confidence of the investors. Many traditional approaches to data analysis in crime-related studies have been done in South Africa, but the machine learning approach has not been adequately considered. The police station and many other agencies that deal with crime hold a lot of databases that can be used to predict or analyze criminal happenings across the provinces of South Africa. This research work aimed at offering a solution to the problem by building a model that can predict crime. The machine learning approach shall be used to extract useful information from South Africa's nine provinces' crime data. A crime prediction system that can analyze and predict crime is proposed. To accomplish this, South Africa crime data on 27 crime categories were obtained from the popular data repository “Kaggle.” Diverse data analytics steps were applied to preprocess the datasets, and a machine learning algorithm (linear regression) was used to build a predictive model to analyze data and predict future crime. The appropriate authorities and security agencies in South Africa can have insight into the crime trends and alleviate them to encourage the foreign stakeholders to continue their businesses. PubDate: Wed, 09 Jun 2021 11:05:01 +000
Abstract: Accurate streamflow estimations are essential for planning and decision-making of many development activities related to water resources. Hydrological modelling is a frequently adopted and a matured technique to simulate streamflow compared to the data driven models such as artificial neural networks (ANNs). In addition, usage of ANNs is minimum to simulate streamflow in the context of Sri Lanka. Therefore, this study presents an intercomparison between streamflow estimations from conventional hydrological modelling and ANN analysis for Seethawaka River Basin located in the upstream part of the Kelani River Basin, Sri Lanka. The hydrological model was developed using the Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS), while the data-driven ANN model was developed in MATLAB. The rainfall and streamflows’ data for 2003–2010 period have been used. The simulations by HEC-HMS were performed by four types of input rainfall data configurations, including observed rainfall data sets and three satellite-based precipitation products (SbPPs), namely, PERSIANN, PERSIANN-CCS, and PERSIANN-CDR. The ANN model was trained using three well-known training algorithms, namely, Levenberg–Marquadt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG). Results revealed that the simulated hydrological model based on observed rainfall outperformed those of based on remotely sensed SbPPs. BR algorithm-based ANN algorithm was found to be superior among the data-driven models in the context of ANN model simulations. However, none of the above developed models were able to capture several peak discharges recorded in the Seethawaka River. The results of this study indicate that ANN models can be used to simulate streamflow to an acceptable level, despite presence of intensive spatial and temporal data sets, which are often required for hydrologic software. Hence, the results of the current study provide valuable feedback for water resources’ planners in the developing region which lack multiple data sets for hydrologic software. PubDate: Fri, 28 May 2021 07:20:01 +000
Abstract: In this paper, the generalized concept of conformable fractional derivatives of order for fuzzy functions is introduced. We presented the definition and proved properties and theorems of these derivatives. The fuzzy conformable fractional differential equations and the properties of the fuzzy solution are investigated, developed, and proved. Some examples are provided for both the new solutions. PubDate: Thu, 27 May 2021 10:35:01 +000
Abstract: In this paper, two intelligent methods which are GAs and PSO are used to model noise coupling in a Three-Dimensional Integrated Circuit (3D-IC) based on TSVs. These techniques are rarely used in this type of structure. They allow computing all the elements of the noise model, which helps to estimate the noise transfer function in the frequency and time domain in 3D complicated systems. Noise models include TSVs, active circuits, and substrate, which make them difficult to model and to estimate. Indeed, the proposed approaches based on GA and PSO are robust and powerful. To validate the method, comparisons among the results found by GA, PSO, measurements, and the 3D-TLM method, which presents an analytical technique, are made. According to the obtained simulation and experimental results, it is found that the proposed methods are valid, efficient, precise, and robust. PubDate: Mon, 24 May 2021 10:35:01 +000
Abstract: The prediction of cardiac conditions can be done through comparison and analysis of parameters transformed into mathematical model equations. This paper aims to present the design of a web-based graphical user interface of mathematical model of cardiovascular-respiratory system (ICRSMM) as an appropriate displaying tool. The designed system offers an easy way of recording and storing parameters in a database. Those parameters are computerized to generate automatic results in a graphic representation, which is an effective way used in medicine to allow physicians, nurses, and other experienced health personnel to analyze and discuss results. The designed solution provides an adequate and friendly environment that eases the task of recording the results in a graphic representation. This gives a clear picture of analysis to determine a healthy or unhealthy cardiovascular-respiratory system of a person exercising. However, such a complex design solution comes in to put an accent of consideration to an area of research that still needs more discoveries and exploration. PubDate: Wed, 05 May 2021 07:35:00 +000
Abstract: Plastic waste management is a challenge for the whole world. Manual sorting of garbage is a difficult and expensive process, which is why scientists create and study automated sorting methods that increase the efficiency of the recycling process. The plastic waste may be automatically chosen on a transmission belt for waste removal by using methods of image processing and artificial intelligence, especially deep learning, to improve the recycling process. Waste segregation techniques and procedures are applied to major groups of materials such as paper, plastic, metal, and glass. Though, the biggest challenge is separating different materials types in a group, for example, sorting different colours of glass or plastics types. The issue of plastic garbage is important due to the possibility of recycling only certain types of plastic (PET can be converted into polyester material). Therefore, we should look for ways to separate this waste. One of the opportunities is the use of deep learning and convolutional neural network. In household waste, the most problematic are plastic components, and the main types are polyethylene, polypropylene, and polystyrene. The main problem considered in this article is creating an automatic plastic waste segregation method, which can separate garbage into four mentioned categories, PS, PP, PE-HD, and PET, and could be applicable on a sorting plant or home by citizens. We proposed a technique that can apply in portable devices for waste recognizing which would be helpful in solving urban waste problems. PubDate: Wed, 05 May 2021 07:20:01 +000
Abstract: COVID-19 is a type of an infectious disease that is caused by the new coronavirus. The spread of COVID-19 needs to be suppressed because COVID-19 can cause death, especially for sufferers with congenital diseases and a weak immune system. COVID-19 spreads through direct contact, wherein the infected individual spreads the COVID-19 virus through cough, sneeze, or close contacts. Predicting the number of COVID-19 sufferers becomes an important task in the effort to curb the spread of COVID-19. Artificial neural network (ANN) is the prediction method that delivers effective results in doing this job. Backpropagation, a type of ANN algorithm, offers predictive problem solving with good performance. However, its performance depends on the optimization method applied during the training process. In general, the optimization method in ANN is the gradient descent method, which is known to have a slow convergence rate. Meanwhile, the Fletcher–Reeves method has a faster convergence rate than the gradient descent method. Based on this hypothesis, this paper proposes a prediction model for the number of COVID-19 sufferers in Malang using the Backpropagation neural network with the Fletcher–Reeves method. The experimental results show that the Backpropagation neural network with the Fletcher–Reeves method has a better performance than the Backpropagation neural network with the gradient descent method. This is shown by the Means Square Error (MSE) resulting from the proposed method which is smaller than the MSE resulting from the Backpropagation neural network with the gradient descent method. PubDate: Thu, 29 Apr 2021 06:35:01 +000
Abstract: This paper introduces an efficient deblurring image method based on a convolution-based and an iterative concept. Our method does not require specific conditions on images, so it can be widely applied for unspecific generic images. The kernel estimation is firstly performed and then will be used to estimate a latent image in each iteration. The final deblurred image is obtained from the convolution of the blurred image with the final estimated kernel. However, image deblurring is an ill-posed problem due to the nonuniqueness of solutions. Therefore, we propose a smoothing function, unlike previous approaches that applied piecewise functions on estimating a latent image. In our approach, we employ L2-regularization on intensity and gradient prior to converging to a solution of the deblurring problem. Moreover, our work is based on the quadratic splitting method. It guarantees that each subproblem has a closed-form solution. Various experiments on synthesized and real-world images confirm that our approach outperforms several existing methods, especially on the images corrupted by noises. Moreover, our method gives more reasonable and more natural deblurred images than those of other methods. PubDate: Sat, 17 Apr 2021 07:35:01 +000
Abstract: In spite of the differences in visual stimulus of human beings such as ageing, changing conditions of a person, and occlusion, recognition can even be done at a glance by the human eye many years after the previous encounter. It has been established that facial differences like the hairstyle changes, growing of one’s beard, wearing of glasses, and other forms of occlusions can hardly hinder the power of the human brain from making a face recognition. However, the same cannot easily be said about automated intelligent systems which have been developed to mimic the skill of the human brain to aid in recognition. There have been growing interests in developing a resilient and efficient recognition system mainly because of its numerous application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). Although there have been numerous researches on face recognition under varying pose, illumination, expression, and image degradations, problems caused by occlusions are mostly ignored. This study thus focuses on facial occlusions and proposes an enhancement mechanism through face image augmentation to improve the recognition of occluded face images. This study assessed the performance of Principal Component Analysis with Singular Value Decomposition using Fast Fourier Transform (FFT-PCA/SVD) for preprocessing face recognition algorithm on face images with missingness and augmented face image database. It was found that the average recognition rates for the FFT-PCA/SVD algorithm were the same () when face images with missingness and augmented face images were used as test images, respectively. The statistical evaluation revealed that there exists a significant difference in the average recognition distances for the face images with missingness and augmented face images when FFT-PCA/SVD is used for recognition. Augmented face images tend to have a relatively lower average recognition distance when used as test images. This finding is contrary to the equal performance assessment by the adopted numerical technique. The MICE algorithm is therefore recommended as a suitable imputation mechanism for enhancing/improving the performance of the face recognition system. PubDate: Sat, 17 Apr 2021 07:20:01 +000
Abstract: Gene expression programming has been applied in this work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R value or Rvalue) of expansive soil treated with an improved composites of rice husk ash. Pavement foundations suffer failures due to poor design and construction, poor materials handling and utilization, and management lapses. The evolution of sustainable green materials and optimization and soft computing techniques have been deployed to improve on the deficiencies being suffered in the abovementioned areas of design and construction engineering. In this work, expansive soil classified as A-7-6 group soil was treated with hydrated-lime activated rice husk ash (HARHA) in an incremental proportion to produce 121 datasets, which were used to predict the behavior of the soil’s strength parameters utilizing the mutative and evolutionary algorithms of GEP. The input parameters were HARHA, liquid limit (), (plastic limit , plasticity index , optimum moisture content (), clay activity (AC), and (maximum dry density (δmax) while CBR, UCS, and R value were the output parameters. A multiple linear regression (MLR) was also conducted on the datasets in addition to GEP to serve as a check mechanism. At the end of the computing and iterations, MLR and GEP optimization methods proposed three equations corresponding to the output parameters of the work. The responses validation on the predicted models shows a good correlation above 0.9 and a great performance index. The predicted models’ performance has shown that GEP soft computing has predicted models that can be used in the design of CBR, UCS, and R value for soils being used as foundation materials and being treated with admixtures as a binding component. PubDate: Wed, 14 Apr 2021 10:50:01 +000
Abstract: This paper is devoted to the development of a deep learning- (DL-) based model to detect crack fractures on concrete surfaces. The developed model for the classification of images was based on a DL Convolutional Neural Network (CNN). To train and validate the CNN model, a database containing 40,000 images of concrete surfaces (with and without cracks) was collected from the available literature. Several conditions on the concrete surfaces were taken into account such as illumination and surface finish (i.e., exposed, plastering, and paint). Various error measurement criteria such as accuracy, precision, recall, specificity, and F1-score were employed for accessing the quality of the developed model. Results showed that for the training dataset (50% of the database), the precision, recall, specificity, F1-score, and accuracy were 99.5%, 99.8%, 99.5%, 99.7%, and 99.7%, respectively. On the other hand, for the validating dataset, the precision, recall, specificity, F1-score, and accuracy are 96.5%, 98.8%, 96.6%, 97.7%, and 97.7%, respectively. Thus, the developed CNN model may be considered valid because it performs the classification of cracks well using the testing data. It is also confirmed that the developed DL-based model was robust and efficient, as it can take into account different conditions on the concrete surfaces. The CNN model developed in this study was compared with other works in the literature, showing that the CNN model could improve the accuracy of image classification, in comparison with previously published results. Finally, in further work, such model could be combined with Unmanned Aerial Vehicles (UAVs) to increase the productivity of concrete infrastructure inspection. PubDate: Fri, 26 Mar 2021 11:35:00 +000
Abstract: Chatbots have been used in many fields ranging from education to healthcare and are also used in e-commerce settings. This research aims at developing a web-based chatbot called Hebron for the Covenant University Community Mall. The chatbot is developed using Python and React.js as the programming languages and MySQL (Structured Query Language) server as the database to give a structure to the e-commerce datasets and Admin Portal process. The e-commerce chatbot application for Covenant University Shopping Mall (CUSM) seeks to provide an easy, smart, and comfortable shopping experience for the Covenant University Community. PubDate: Sat, 20 Mar 2021 08:35:01 +000
Abstract: Amidst the wide spectrum of recognition methods proposed, there is still the challenge of these algorithms not yielding optimal accuracy against illumination, pose, and facial expression. In recent years, considerable attention has been on the use of swarm intelligence methods to help resolve some of these persistent issues. In this study, the principal component analysis (PCA) method with the inherent property of dimensionality reduction was adopted for feature selection. The resultant features were optimized using the particle swarm optimization (PSO) algorithm. For the purpose of performance comparison, the resultant features were also optimized with the genetic algorithm (GA) and the artificial bee colony (ABC). The optimized features were used for the recognition using Euclidean distance (EUD), K-nearest neighbor (KNN), and the support vector machine (SVM) as classifiers. Experimental results of these hybrid models on the ORL dataset reveal an accuracy of 99.25% for PSO and KNN, followed by ABC with 93.72% and GA with 87.50%. On the central, an experimentation of the PSO, GA, and ABC on the YaleB dataset results in 100% accuracy demonstrating their efficiencies over the state-of-the art methods. PubDate: Fri, 12 Mar 2021 11:20:00 +000
Abstract: Different types of site selection exploited by geographic information systems (GIS) by combining various types of data relate to the purpose of site selection. In reality, numerous factors including physical, environmental, and social factors affect the site selection in terms of deciding the location of a new industry. Accordingly, this paper conceives systems for determining new industrial zones in the form of web-based applications or ordinarily called web-based GIS. Thus, the application named the potential industrial zone smart systems, in which the prototype of the application is able to facilitate planning the determination of new industrial zone based on six parameters throughout the analytical hierarchy process method. The result weights of six criteria are soil type 35%, land use 32%, land slope 15%, the distance of land to river 9%, the distance of land from road and accessibility 5%, and the distance of land to public facilities 4%. Additionally, the web-based GIS is a user-friendly application to determine the planned industrial location. Further, the demonstration runs effortlessly in exhibiting data on the potential of new industrial zones in the city of Bekasi, West Java Province of Indonesia. PubDate: Fri, 05 Mar 2021 15:20:01 +000
Abstract: Understanding the determinants of satisfaction in P2P hosting is crucial, especially with the emergence of platforms such as Airbnb, which has become the largest platform for short-term rental accommodation. Although many studies have been carried out in this direction, there are still gaps to be filled, particularly with regard to the apprehension of customers taking into account their category. In this study, we took a machine learning-based approach to examine 100,000 customer reviews left on the Airbnb platform to identify different dimensions that shape customer satisfaction according to each category studied (individuals, couples, and families). However, the data collected do not give any information on the category to which the customer belongs to. So, we applied natural language processing (NLP) algorithms to the reviews in order to find clues that could help us segment them, and then we trained two regression models, multiple linear regression and support vector regression, in order to calculate the coefficients acting on each of the 6 elementary scores (precision, cleanliness, check-in, communication, location, and value) noted on Airbnb, taking into account the category of customers who evaluated the performance of their accommodation. The results suggest that customers are not equally interested in satisfaction metrics. In addition, disparities were noted for the same indicator depending on the category to which the client belongs to. In light of these results, we suggest that improvements be made to the rating system adopted by Airbnb to make it suitable for each category to which the client belongs to. PubDate: Sat, 20 Feb 2021 14:20:01 +000
Abstract: Grading is the process of interpreting learning competence to inform learners and instructors of the current learning ability levels and necessary improvement. For norm-referenced grading, the instructors use a conventionally statistical method, z score. It is difficult for such a method to achieve explainable grade discrimination to resolve dispute between learners and instructors. To solve such difficulty, this paper proposes a simple and efficient algorithm for explainable norm-referenced grading. Moreover, the rise of artificial intelligence nowadays makes machine learning techniques attractive to the norm-referenced grading in general. This paper also investigates two popular clustering methods, K-means and partitioning around medoids. The experiment relied on the data sets of various score distributions and a metric, namely, Davies–Bouldin index. The comparative evaluation reveals that our algorithm overall outperforms the other three methods and is appropriate for all kinds of data sets in almost all cases. Our findings however lead to a practically useful guideline for the selection of appropriate grading methods including both clustering methods and z score. PubDate: Sat, 20 Feb 2021 12:50:01 +000
Abstract: One of the main economic issues in Iran is the issue of economic sanctions. These sanctions have been imposed by various institutions and countries around the world in various forms since 1979 against Iran. Economic sanctions have affected large sections of Iran’s economy. Meanwhile, economic sanctions against Iran have had far-reaching effects on trade cycles in Iran. The purpose of this article is to investigate the impact of economic sanctions on the structure of business cycles in Iran. The sanction index is a tool for studying quantitative sanctions. The opinions of 15 experts in sanctions economics were collected using fuzzy questionnaires. And the sanction index was obtained. The fuzzy logic method in the MATLAB software space calculated the economic sanction index for 1979–2019. The self-regression calculated the effect of economic sanctions on business cycles. There are two scenarios in this article. In scenario 1, sanctions increased inflation, reduced production, and reduced investment. Also, during the embargo period, the recessions are longer. The second scenario of the research shows the economy without sanctions. The results showed that, in these conditions, inflation has less effect on production and investment. And the economy will experience a long period of prosperity without sanctions. PubDate: Sat, 30 Jan 2021 04:05:00 +000
Abstract: Recently, the problem of Virtual Machine Placement (VMP) has received enormous attention from the research community due to its direct effect on the energy efficiency, resource utilization, and performance of the cloud data center. VMP is considered as a multidimensional bin packing problem, which is a type of NP-hard problem. The challenge in VMP is how to optimally place multiple independent virtual machines into a few physical servers to maximize a cloud provider’s revenue while meeting the Service Level Agreements (SLAs). In this paper, an effective multiobjective algorithm based on Particle Swarm Optimization (PSO) technique for the VMP problem, referred to as VMPMOPSO, is proposed. The proposed VMPMOPSO utilizes the crowding entropy method to optimize the VMP and to improve the diversity among the obtained solutions as well as accelerate the convergence speed toward the optimal solution. VMPMOPSO was compared with a simple single-objective algorithm, called First-Fit-Decreasing (FFD), and two multiobjective ant colony and genetic algorithms. Two simulation experiments were conducted to verify the effectiveness and efficiency of the proposed VMPMOPSO. The first experiment shows that the proposed algorithm has better performance than the algorithms we compared it to in terms of power consumption, SLA violation, and resource wastage. The second indicates that the Pareto optimal solutions obtained by applying VMPMOPSO have a good distribution and a better convergence than the comparative algorithms. PubDate: Wed, 13 Jan 2021 13:50:01 +000
Open Access journal
[343 journals]
